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ACE2047/README.md

💫 About Me:

I'm currently working on diving deep into machine learning models, exploring various architectures from neural networks to ensemble methods. Currently focusing on understanding the nuanced differences between supervised, unsupervised, and reinforcement learning approaches.
I'm looking to collaborate on ML model development and research. Excited to connect with fellow machine learning enthusiasts who are passionate about comparing different model architectures, sharing insights, and exploring cutting-edge techniques in AI.
I'm currently learning to expand my knowledge of machine learning models, working on practical implementations, and understanding the theoretical foundations behind different ML algorithms and their real-world applications.
Ask me about my machine learning learning journey, the ML models I'm currently studying, and my approaches to understanding complex AI concepts.
I'm looking for help with seeking opportunities with forward-thinking tech companies willing to support emerging ML talent. Looking for internships, junior roles, or mentorship programs that provide hands-on experience in machine learning, where I can grow my skills while contributing to innovative projects.

🌐 Socials:

Instagram LinkedIn Stack Overflow email

💻 Tech Stack:

C C# C++ Java HTML5 PowerShell Python TypeScript JavaScript GraphQL MySQL PowerShell Postgres Power Bi AWS Windows Terminal Google Cloud Oracle Angular Type-graphql Apache Airflow Apache Flink Blender Adobe Lightroom Adobe Illustrator Adobe Creative Cloud Matplotlib mlflow scikit-learn TensorFlow Plotly GitLab CI GitLab Docker Flask NumPy .Net Chart.js Context-API Bootstrap Express.js FastAPI Flutter JavaFX NestJS Next JS NodeJS React React Query TailwindCSS Jenkins MicrosoftSQLServer Pandas PyTorch Scipy Git GitHub Actions GitHub Apache Subversion Terraform Steam Unity AMD Notion Spring Cloudflare CSS3 Kotlin Flask JWT Docker

📊 GitHub Stats:



✍️ Random Dev Quote

🔝 Top Contributed Repo


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    Using ANN and Random Forest models I was able to tap into the Online Shopping Behaviors of customers from 2019 October to April of 2020. With an accuracy of between 85% to 92%.

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